Base Date 2026-11-03
Last Run 2026-11-03 22:30:03
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35.4%
S&P 500
7,569
NDX 100
28,180
VIX
20.83
US 10Y
4.95%
DXY
101.87
WTI
$96.76
Gold
$3,940
USD/KRW
1,344
SOX
10,600
Trump Approve
35.4%
AI LEADERS · MOVES & PAPERS

AI Leaders: Moves & Papers — latest job changes and research

LAST UPDATE 2026-11-04 08:05 KST PEOPLE 77 · PAPERS 76 · MOVES 0
Where the key people at AI labs, big tech and universities move, and what they publish, are early signals of where technology and competitive advantage are heading. Career moves and papers are collected automatically every day, checked by the editor and summarized in plain language with links to the originals.

Highlights

Career moves · Academia · AI for science

No career moves match these filters yet.

Papers · Academia · AI for science

Paper2026-10-12 · arXiv (Cornell University)

Development of a Technology for Efficiently Discovering Diverse Antibody Candidates Using Artificial Intelligence

Multi-Objective Amortized CDRH3 Sequence Sampling Diversifies Promising Antibody Candidates
What
Researchers have developed a new artificial intelligence-based antibody sequence generation technology that can simultaneously consider multiple conditions and diversity during the antibody design process.
Key result
Compared to existing methods, it generated 10 times more diverse and acceptable antibody candidates at a 50 percent sequence identity.
Why it matters
This research can help reduce the probability of failure in the early stages of new drug development and explore various potential antibody candidates.AI summary
Paper2026-10-08 · Code Ocean

Technology to Interpret the Causes of Uncertainty in Artificial Intelligence Revealed

pce-uncertainty-attribution: code for "Interpretable attribution of decision-relevant uncertainty in AI"
What
Released code and methods to interpret which factors contribute to the decision-making uncertainty of trained artificial intelligence models.
Key result
Utilized polynomial surrogates to uncover the causes of model uncertainty in the fields of chemistry, experimental design, and language model alignment.
Why it matters
Can be utilized to better understand the decision-making process of artificial intelligence models across various fields and increase reliability.AI summary
Paper2026-10-04 · arXiv (Cornell University)

Measuring how easily AI comes up with future research ideas

Priced Guidance: Can Language Models Generate Future Research Ideas?
Percy Liang +2 more
What
The study proposes a method to indirectly estimate the likelihood that a language model generates novel research ideas without hints, using the amount of information (compression cost) required to reveal the correct answer.
Key result
Five models were evaluated on the core ideas of 87 recent deep learning papers, and the median compression cost of the best single model was 69.9 bits. This is significantly lower than the median of 5,712 bits for the general compression tool gzip.
Why it matters
This research demonstrates a new evaluation framework that can calculate the lower bound of the probability that a model generates ideas. However, the abstract does not address research productivity or market effects.AI summary

People

Why follow AI leaders' moves and papers?

In AI, people are the technology. Which company a key researcher joins, or which lab they found, often turns into products, funding rounds and acquisitions months later. This page tracks job changes across frontier labs, new labs, academia, AI chips, and people in Korea and China.

Papers show what a person is focused on right now. We sort papers into 10 investable fields such as HBM & memory, GPUs & accelerators, inference efficiency, agents and robotics, and give a three-line summary — what, key result, why it matters — based on the abstract, with links to the original and PDF. This is information for finding ideas, not a recommendation to buy or sell any security.

Frequently Asked Questions

Q. Who do you track?

Key researchers and executives at frontier labs (OpenAI, Google DeepMind, Anthropic, Meta and others), new labs, academia, AI chip companies, and in Korea and China. Executives who rarely publish are tracked for career moves only.

Q. Where does the data come from, and how often is it updated?

Papers come from Semantic Scholar and arXiv, and careers from Wikidata (CC0) plus official announcements and press reports, collected automatically every day. Only items checked and approved by the editor are published.

Q. Who writes the paper summaries?

An AI (Google Gemini) drafts each summary from the paper's abstract, and the editor checks it before publishing. Other languages are AI translations of the checked Korean summary. Always confirm exact figures and context in the original paper.

This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.

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